A GCN-LSTM Approach for ES-mini and VX Futures Forecasting

ArXiv ID: 2408.05659 “View on arXiv”

Authors: Unknown

Abstract

We propose a novel data-driven network framework for forecasting problems related to E-mini S&P 500 and CBOE Volatility Index futures, in which products with different expirations act as distinct nodes. We provide visual demonstrations of the correlation structures of these products in terms of their returns, realized volatility, and trading volume. The resulting networks offer insights into the contemporaneous movements across the different products, illustrating how inherently connected the movements of the future products belonging to these two classes are. These networks are further utilized by a multi-channel Graph Convolutional Network to enhance the predictive power of a Long Short-Term Memory network, allowing for the propagation of forecasts of highly correlated quantities, combining the temporal with the spatial aspect of the term structure.

Keywords: Graph Convolutional Network (GCN), Long Short-Term Memory (LSTM), Term Structure, E-mini S&P 500, CBOE Volatility Index (VIX)

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced machine learning architectures (GCN-LSTM) and graph theory to model complex, high-dimensional temporal-spatial dependencies, which involves non-trivial mathematical formulations. It is also heavily data-driven, with explicit details on data preprocessing, graph construction, hyperparameter tuning, multiple evaluation metrics, and backtesting on real financial data (ES-mini and VX futures).

  flowchart TD
    A["Research Goal:<br/>Forecast ES & VX Futures"] --> B["Data Sources<br/>ES-mini & VX Futures"]
    B --> C["Network Construction<br/>Correlation-based Graph"]
    C --> D["Spatial-Temporal Modeling<br/>GCN-LSTM Hybrid"]
    D --> E{"Training & Validation"}
    E --> F["Key Findings"]
    
    subgraph F ["Outcomes"]
        F1["Enhanced Forecast Accuracy"]
        F2["Interpretable Term Structure"]
        F3["Capturing Cross-Market Dynamics"]
    end

    style A fill:#e1f5fe,stroke:#01579b,stroke-width:2px
    style F fill:#f1f8e9,stroke:#33691e,stroke-width:2px